**Moriah Harman, Redistributive Policy Preferences: The Effects of Family Income and Poverty Attributions, Data Replication

**Analysis**

**Table 1: Logistic regression estimates for models of Americans' attitudes toward raising taxes on high-income Americans

**Table 1, Model 1 (raise taxes on the wealthy, no interactions):
logit raisewealthytax famincome19 fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Table 1, Model 2 (raise taxes on the wealthy, with interactions):
logit raisewealthytax c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Main Text, Figure 1:
margins, at(fpoverty1 = (-2.01) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend
margins, at(fpoverty1 = (2.12) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend

twoway (scatter predpovlow income, connect(l)) (scatter upperpovlow income, connect(l)) (scatter lowerpovlow income, connect(l)) (scatter predpovhigh income, connect(l)) (scatter upperpovhigh income, connect(l)) (scatter lowerpovhigh income, connect(l))


**Now for an alternative interaction interpretation -- that family income moderates the effect of poverty attributions. Running the model first:
logit raisewealthytax c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Now for an alternative figure (Main Text, Figure 2)
margins, at(famincome19 = (0) fpoverty1 = (-2.01 -1.51 -1.01 -0.51 -0.01 0.49 0.99 1.49 1.99 2.12)) atmeans noatlegend 
margins, at(famincome19 = (15) fpoverty1 = (-2.01 -1.51 -1.01 -0.51 -0.01 0.49 0.99 1.49 1.99 2.12)) atmeans noatlegend

twoway (scatter predinclow poverty, connect(l)) (scatter upperinclow poverty, connect(l)) (scatter lowerinclow poverty, connect(l)) (scatter predinchigh poverty, connect(l)) (scatter upperinchigh poverty, connect(l)) (scatter lowerinchigh poverty, connect(l))


**Appendix Table A3, Model 1 (raise taxes on the wealthy, no interactions), using OLS:
regress raisewealthytax famincome19 fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Appendix Table A3, Model 2 (raise taxes on the wealthy, interactions), using OLS:
regress raisewealthytax c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Appendix Figure A4 (following OLS): 
margins, at(fpoverty1 = (-2.01) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend
margins, at(fpoverty1 = (2.12) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend
twoway (scatter predpovlow income, connect(l)) (scatter upperpovlow income, connect(l)) (scatter lowerpovlow income, connect(l)) (scatter predpovhigh income, connect(l)) (scatter upperpovhigh income, connect(l)) (scatter lowerpovhigh income, connect(l))


**Running the original ordered logit models for DV2, which I moved to the appendix
**Appendix Table A4, Model 1 (providing tax credits to lower-income workers, no interactions, ologit):
ologit taxcreditsforlowincome famincome19 fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Appendix Table A4, Model 2(providing tax credits to lower-income workers, interaction included, ologit):
ologit taxcreditsforlowincome c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Appendix Figure A5 (following ologit):
**Steps for creating Appendix Figure A5 (using predicted probabilities for each outcome on an ordinal scale to generate means for that DV, which can then be graphed)
ologit taxcreditsforlowincome c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

mtable, at(fpoverty1 = (-2.01) famincome19 = (0 3 6 9 12 15)) atmeans noatlegend
mtable, at(fpoverty1 = (2.12) famincome19 = (0 3 6 9 12 15)) atmeans noatlegend

gen meanlow = (0 * prob0low) + (1 * prob1low) + (2 * prob2low) + (3 * prob3low) + (4 * prob4low) 
gen meanhigh = (0 * prob0high) + (1 * prob1high) + (2 * prob2high) + (3 * prob3high) + (4 * prob4high) 
**Figure A5:
twoway (scatter meanlow income, connect(l))  (scatter meanhigh income, connect(l)) 


**Main-Text Table 2
**Main-Text Table 2, Model 1 (providing tax credits, no interactions), OLS:
regress taxcreditsforlowincome famincome19 fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**Main-Text Table 2, Model 2 (providing tax credits, interactions), OLS:
regress taxcreditsforlowincome c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

**UPDATED Main Figure 3 (using OLS, in the main text):
margins, at(fpoverty1 = (-2.01) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend
margins, at(fpoverty1 = (2.12) famincome19 = (0 2 4 6 8 10 12 14 15)) atmeans noatlegend
**copied and pasted each output into an excel spreadsheet, cleaned it up, then copied and pasted that data into a new Stata dataset and ran the following:

twoway (scatter predpovlow income, connect(l)) (scatter upperpovlow income, connect(l)) (scatter lowerpovlow income, connect(l)) (scatter predpovhigh income, connect(l)) (scatter upperpovhigh income, connect(l)) (scatter lowerpovhigh income, connect(l))

**Now, for an alternative interaction interpretation (per reviewer feedback) (Main Text Figure 4):
regress taxcreditsforlowincome c.famincome19##c.fpoverty1 partyid libcon polinterest19 educ19 econtrend19 age19 gender19 black hispanic asian chattend19

margins, at(famincome19 = (0) fpoverty1 = (-2.01 -1.51 -1.01 -0.51 -0.01 0.49 0.99 1.49 1.99 2.12)) atmeans noatlegend 
margins, at(famincome19 = (15) fpoverty1 = (-2.01 -1.51 -1.01 -0.51 -0.01 0.49 0.99 1.49 1.99 2.12)) atmeans noatlegend

twoway (scatter predinclow poverty, connect(l)) (scatter upperinclow poverty, connect(l)) (scatter lowerinclow poverty, connect(l)) (scatter predinchigh poverty, connect(l)) (scatter upperinchigh poverty, connect(l)) (scatter lowerinchigh poverty, connect(l))

